Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

Chi, Mingmin; Qian, Kun; Benediktsson, Jón Atli; Feng, Rui

doi:10.1109/lgrs.2009.2024624

Cited by 45 publications

(6 citation statements)

References 7 publications

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“…The RF algorithm belongs to the ensemble learning model, which integrates multiple base classifiers to make joint decisions [24]. Ensemble learning has demonstrated superior performance in various fields [25]. The Rotation Forest (ROF) algorithm [26], an extension of the RF algorithm, is another integrated learning model.…”

Section: Introductionmentioning

confidence: 99%

Classifying Mountain Vegetation Types Using Object-Oriented Machine Learning Methods Based on Different Feature Combinations

Zhou

et al. 2023

Forests

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Mountainous vegetation type classification plays a fundamental role in resource investigation in forested areas, making it necessary to accurately identify mountain vegetation types. However, Mountainous vegetation growth is readily affected by terrain and climate, which often makes interpretation difficult. This study utilizes Sentinel-2A images and object-oriented machine learning methods to map vegetation types in the complex mountainous region of Jiuzhaigou County, China, incorporating multiple auxiliary features. The results showed that the inclusion of different features improved the accuracy of mountain vegetation type classification, with terrain features, vegetation indices, and spectral features providing significant benefits. After feature selection, the accuracy of mountain vegetation type classification was further improved. The random forest recursive feature elimination (RF_RFE) algorithm outperformed the RliefF algorithm in recognizing mountain vegetation types. Extreme learning machine (ELM), random forest (RF), rotation forest (ROF), and ROF_ELM algorithms all achieved good classification performance, with an overall accuracy greater than 84.62%. Comparing the mountain vegetation type distribution maps obtained using different classifiers, we found that classification algorithms with the same base classifier ensemble exhibited similar performance. Overall, the ROF algorithm performed the best, achieving an overall accuracy of 89.68%, an average accuracy of 88.48%, and a Kappa coefficient of 0.8789.

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Section: Introductionmentioning

confidence: 99%

Classifying Mountain Vegetation Types Using Object-Oriented Machine Learning Methods Based on Different Feature Combinations

Zhou

et al. 2023

Forests

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“…In terms of classifier ensemble technology, two strategies, namely "multiple classifier systems" (Benediktsson, 2009) and "decision fusion" (Fauvel et al, 2006) are employed. Multiple classifier systems are based on the manipulation of training sample sets, including boosting (Freund et al, 2003) and bagging (Breiman, 1996).…”

Section: Introductionmentioning

confidence: 99%

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

Zhang

Pan

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

322

154

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The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and texturalbased MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

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“…However, most of these approaches rely on one regression model for the prediction, and are therefore subject to overfitting when the training data is limited (Pal, 2007). In the machine learning community, there is an increasing interest in combining several base learning models into one predictive model in order to improve the model generalization ability (Chi et al, 2009; Zhou, 2009; Zhang and Crawford, 2015). By combining multiple learners, the errors of a single model will likely be compensated by others, and thus help improve the robustness and accuracy of the prediction.…”

Section: Introductionmentioning

confidence: 99%

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach

et al. 2019

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California’s almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination ( R 2 ) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April–June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability.

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Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

Cited by 45 publications

References 7 publications

Classifying Mountain Vegetation Types Using Object-Oriented Machine Learning Methods Based on Different Feature Combinations

Classifying Mountain Vegetation Types Using Object-Oriented Machine Learning Methods Based on Different Feature Combinations

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach

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